Abstract This paper advances a focused and revisable thesis: under conditions of basic connectivity and literacy, AI systems are reconfiguring the locus of constraint on epistemic access—shifting it, increasingly, from structural position toward personal intention. This reconfiguration—made possible by AI systems capable of synthesizing the global scientific consensus in natural language—constitutes what I call the Intention Turn in epistemic access. A crucial clarification distinguishes this turn from previous democratizing technologies. The printing press democratized access to recorded information. The internet democratized access to raw information at scale. AI democratizes something categorically different: access to the synthesis and understanding of that information. I situate this argument within Miranda Fricker’s framework of distributive epistemic injustice, proposing a corrective inversion: where existing scholarship documents how AI perpetuates epistemic injustice, I argue that AI enables the first scalable mechanism capable of producing a partial distributive epistemic correction—a historically significant, if fragile and conditional, shift in who can access the epistemic commons. I further introduce the principle of Epistemic Intent Amplification (EIA)—the claim that AI systems magnify whatever epistemic orientation a user brings to them, functioning simultaneously as descriptive principle and normative diagnostic—and argue that this principle has significant implications for how I theorize individual responsibility in an AI-mediated epistemic environment. The paper makes three contributions: a conceptual framework (the Intention Turn), a normative-descriptive principle (EIA), and a three-layer account of distributed epistemic responsibility with direct implications for AI system design.
Lior Gazit (Tue,) studied this question.